Overview

Dataset statistics

Number of variables13
Number of observations4269
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory274.5 B

Variable types

Numeric10
Categorical3

Alerts

income_annum is highly overall correlated with loan_amount and 4 other fieldsHigh correlation
loan_amount is highly overall correlated with income_annum and 4 other fieldsHigh correlation
cibil_score is highly overall correlated with loan_statusHigh correlation
residential_assets_value is highly overall correlated with income_annum and 3 other fieldsHigh correlation
commercial_assets_value is highly overall correlated with income_annum and 3 other fieldsHigh correlation
luxury_assets_value is highly overall correlated with income_annum and 4 other fieldsHigh correlation
bank_asset_value is highly overall correlated with income_annum and 4 other fieldsHigh correlation
loan_status is highly overall correlated with cibil_scoreHigh correlation
loan_id is uniformly distributedUniform
loan_id has unique valuesUnique
no_of_dependents has 712 (16.7%) zerosZeros
residential_assets_value has 45 (1.1%) zerosZeros
commercial_assets_value has 107 (2.5%) zerosZeros

Reproduction

Analysis started2023-09-05 13:43:52.553887
Analysis finished2023-09-05 13:44:19.954320
Duration27.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

loan_id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct4269
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2135
Minimum1
Maximum4269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2023-09-05T16:44:20.199100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile214.4
Q11068
median2135
Q33202
95-th percentile4055.6
Maximum4269
Range4268
Interquartile range (IQR)2134

Descriptive statistics

Standard deviation1232.4985
Coefficient of variation (CV)0.57728266
Kurtosis-1.2
Mean2135
Median Absolute Deviation (MAD)1067
Skewness0
Sum9114315
Variance1519052.5
MonotonicityStrictly increasing
2023-09-05T16:44:20.523839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2852 1
 
< 0.1%
2838 1
 
< 0.1%
2839 1
 
< 0.1%
2840 1
 
< 0.1%
2841 1
 
< 0.1%
2842 1
 
< 0.1%
2843 1
 
< 0.1%
2844 1
 
< 0.1%
2845 1
 
< 0.1%
Other values (4259) 4259
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4269 1
< 0.1%
4268 1
< 0.1%
4267 1
< 0.1%
4266 1
< 0.1%
4265 1
< 0.1%
4264 1
< 0.1%
4263 1
< 0.1%
4262 1
< 0.1%
4261 1
< 0.1%
4260 1
< 0.1%

no_of_dependents
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4987116
Minimum0
Maximum5
Zeros712
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2023-09-05T16:44:20.787174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6959102
Coefficient of variation (CV)0.67871383
Kurtosis-1.2569922
Mean2.4987116
Median Absolute Deviation (MAD)1
Skewness-0.017970543
Sum10667
Variance2.8761113
MonotonicityNot monotonic
2023-09-05T16:44:21.047202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 752
17.6%
3 727
17.0%
0 712
16.7%
2 708
16.6%
1 697
16.3%
5 673
15.8%
ValueCountFrequency (%)
0 712
16.7%
1 697
16.3%
2 708
16.6%
3 727
17.0%
4 752
17.6%
5 673
15.8%
ValueCountFrequency (%)
5 673
15.8%
4 752
17.6%
3 727
17.0%
2 708
16.6%
1 697
16.3%
0 712
16.7%

education
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.6 KiB
Graduate
2144 
Not Graduate
2125 

Length

Max length13
Median length9
Mean length10.991099
Min length9

Characters and Unicode

Total characters46921
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Graduate
2nd row Not Graduate
3rd row Graduate
4th row Graduate
5th row Not Graduate

Common Values

ValueCountFrequency (%)
Graduate 2144
50.2%
Not Graduate 2125
49.8%

Length

2023-09-05T16:44:21.353868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T16:44:21.640906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
graduate 4269
66.8%
not 2125
33.2%

Most occurring characters

ValueCountFrequency (%)
a 8538
18.2%
6394
13.6%
t 6394
13.6%
G 4269
9.1%
r 4269
9.1%
d 4269
9.1%
u 4269
9.1%
e 4269
9.1%
N 2125
 
4.5%
o 2125
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34133
72.7%
Space Separator 6394
 
13.6%
Uppercase Letter 6394
 
13.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8538
25.0%
t 6394
18.7%
r 4269
12.5%
d 4269
12.5%
u 4269
12.5%
e 4269
12.5%
o 2125
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
G 4269
66.8%
N 2125
33.2%
Space Separator
ValueCountFrequency (%)
6394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40527
86.4%
Common 6394
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8538
21.1%
t 6394
15.8%
G 4269
10.5%
r 4269
10.5%
d 4269
10.5%
u 4269
10.5%
e 4269
10.5%
N 2125
 
5.2%
o 2125
 
5.2%
Common
ValueCountFrequency (%)
6394
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8538
18.2%
6394
13.6%
t 6394
13.6%
G 4269
9.1%
r 4269
9.1%
d 4269
9.1%
u 4269
9.1%
e 4269
9.1%
N 2125
 
4.5%
o 2125
 
4.5%

self_employed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.4 KiB
Yes
2150 
No
2119 

Length

Max length4
Median length4
Mean length3.5036308
Min length3

Characters and Unicode

Total characters14957
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row No
2nd row Yes
3rd row No
4th row No
5th row Yes

Common Values

ValueCountFrequency (%)
Yes 2150
50.4%
No 2119
49.6%

Length

2023-09-05T16:44:21.903134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T16:44:22.147981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
yes 2150
50.4%
no 2119
49.6%

Most occurring characters

ValueCountFrequency (%)
4269
28.5%
Y 2150
14.4%
e 2150
14.4%
s 2150
14.4%
N 2119
14.2%
o 2119
14.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6419
42.9%
Space Separator 4269
28.5%
Uppercase Letter 4269
28.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2150
33.5%
s 2150
33.5%
o 2119
33.0%
Uppercase Letter
ValueCountFrequency (%)
Y 2150
50.4%
N 2119
49.6%
Space Separator
ValueCountFrequency (%)
4269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10688
71.5%
Common 4269
 
28.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 2150
20.1%
e 2150
20.1%
s 2150
20.1%
N 2119
19.8%
o 2119
19.8%
Common
ValueCountFrequency (%)
4269
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4269
28.5%
Y 2150
14.4%
e 2150
14.4%
s 2150
14.4%
N 2119
14.2%
o 2119
14.2%

income_annum
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5059123.9
Minimum200000
Maximum9900000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2023-09-05T16:44:22.438720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200000
5-th percentile600000
Q12700000
median5100000
Q37500000
95-th percentile9400000
Maximum9900000
Range9700000
Interquartile range (IQR)4800000

Descriptive statistics

Standard deviation2806839.8
Coefficient of variation (CV)0.55480749
Kurtosis-1.182729
Mean5059123.9
Median Absolute Deviation (MAD)2400000
Skewness-0.012814425
Sum2.15974 × 1010
Variance7.8783498 × 1012
MonotonicityNot monotonic
2023-09-05T16:44:22.926610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7000000 62
 
1.5%
4100000 59
 
1.4%
7600000 57
 
1.3%
4700000 56
 
1.3%
6900000 55
 
1.3%
3200000 55
 
1.3%
5300000 55
 
1.3%
3900000 54
 
1.3%
8000000 53
 
1.2%
9000000 53
 
1.2%
Other values (88) 3710
86.9%
ValueCountFrequency (%)
200000 42
1.0%
300000 51
1.2%
400000 35
0.8%
500000 46
1.1%
600000 49
1.1%
700000 45
1.1%
800000 41
1.0%
900000 39
0.9%
1000000 42
1.0%
1100000 45
1.1%
ValueCountFrequency (%)
9900000 35
0.8%
9800000 48
1.1%
9700000 40
0.9%
9600000 39
0.9%
9500000 40
0.9%
9400000 47
1.1%
9300000 33
0.8%
9200000 49
1.1%
9100000 40
0.9%
9000000 53
1.2%

loan_amount
Real number (ℝ)

HIGH CORRELATION 

Distinct378
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15133450
Minimum300000
Maximum39500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2023-09-05T16:44:23.265229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile1800000
Q17700000
median14500000
Q321500000
95-th percentile30900000
Maximum39500000
Range39200000
Interquartile range (IQR)13800000

Descriptive statistics

Standard deviation9043363
Coefficient of variation (CV)0.59757443
Kurtosis-0.74367972
Mean15133450
Median Absolute Deviation (MAD)6900000
Skewness0.30872388
Sum6.46047 × 1010
Variance8.1782414 × 1013
MonotonicityNot monotonic
2023-09-05T16:44:23.642019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10600000 27
 
0.6%
20000000 24
 
0.6%
9400000 24
 
0.6%
16800000 23
 
0.5%
23900000 23
 
0.5%
11500000 22
 
0.5%
14100000 22
 
0.5%
12500000 22
 
0.5%
1800000 21
 
0.5%
3200000 21
 
0.5%
Other values (368) 4040
94.6%
ValueCountFrequency (%)
300000 6
 
0.1%
400000 7
 
0.2%
500000 16
0.4%
600000 13
0.3%
700000 15
0.4%
800000 15
0.4%
900000 12
0.3%
1000000 10
0.2%
1100000 18
0.4%
1200000 17
0.4%
ValueCountFrequency (%)
39500000 1
 
< 0.1%
38800000 1
 
< 0.1%
38700000 2
< 0.1%
38500000 1
 
< 0.1%
38400000 1
 
< 0.1%
38200000 3
0.1%
38000000 1
 
< 0.1%
37900000 2
< 0.1%
37800000 2
< 0.1%
37700000 1
 
< 0.1%

loan_term
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.900445
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2023-09-05T16:44:23.939440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q16
median10
Q316
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7091873
Coefficient of variation (CV)0.52375726
Kurtosis-1.2208527
Mean10.900445
Median Absolute Deviation (MAD)4
Skewness0.036358907
Sum46534
Variance32.594819
MonotonicityNot monotonic
2023-09-05T16:44:24.208106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 490
11.5%
12 456
10.7%
4 447
10.5%
10 436
10.2%
18 422
9.9%
16 412
9.7%
20 411
9.6%
14 405
9.5%
2 404
9.5%
8 386
9.0%
ValueCountFrequency (%)
2 404
9.5%
4 447
10.5%
6 490
11.5%
8 386
9.0%
10 436
10.2%
12 456
10.7%
14 405
9.5%
16 412
9.7%
18 422
9.9%
20 411
9.6%
ValueCountFrequency (%)
20 411
9.6%
18 422
9.9%
16 412
9.7%
14 405
9.5%
12 456
10.7%
10 436
10.2%
8 386
9.0%
6 490
11.5%
4 447
10.5%
2 404
9.5%

cibil_score
Real number (ℝ)

HIGH CORRELATION 

Distinct601
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean599.93605
Minimum300
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2023-09-05T16:44:24.507971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile330
Q1453
median600
Q3748
95-th percentile869
Maximum900
Range600
Interquartile range (IQR)295

Descriptive statistics

Standard deviation172.4304
Coefficient of variation (CV)0.28741463
Kurtosis-1.1856696
Mean599.93605
Median Absolute Deviation (MAD)147
Skewness-0.0090392773
Sum2561127
Variance29732.243
MonotonicityNot monotonic
2023-09-05T16:44:24.841576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
348 16
 
0.4%
543 15
 
0.4%
538 15
 
0.4%
778 14
 
0.3%
509 14
 
0.3%
439 14
 
0.3%
415 14
 
0.3%
819 14
 
0.3%
865 13
 
0.3%
802 13
 
0.3%
Other values (591) 4127
96.7%
ValueCountFrequency (%)
300 11
0.3%
301 8
0.2%
302 13
0.3%
303 6
0.1%
304 8
0.2%
305 3
 
0.1%
306 9
0.2%
307 8
0.2%
308 6
0.1%
309 6
0.1%
ValueCountFrequency (%)
900 6
0.1%
899 6
0.1%
898 5
0.1%
897 4
 
0.1%
896 11
0.3%
895 11
0.3%
894 5
0.1%
893 2
 
< 0.1%
892 8
0.2%
891 8
0.2%

residential_assets_value
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct278
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7472616.5
Minimum-100000
Maximum29100000
Zeros45
Zeros (%)1.1%
Negative28
Negative (%)0.7%
Memory size33.5 KiB
2023-09-05T16:44:25.176085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-100000
5-th percentile300000
Q12200000
median5600000
Q311300000
95-th percentile21260000
Maximum29100000
Range29200000
Interquartile range (IQR)9100000

Descriptive statistics

Standard deviation6503636.6
Coefficient of variation (CV)0.87032923
Kurtosis0.18473799
Mean7472616.5
Median Absolute Deviation (MAD)4100000
Skewness0.9784506
Sum3.19006 × 1010
Variance4.2297289 × 1013
MonotonicityNot monotonic
2023-09-05T16:44:25.529252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400000 66
 
1.5%
500000 63
 
1.5%
100000 60
 
1.4%
1000000 57
 
1.3%
600000 56
 
1.3%
1300000 55
 
1.3%
700000 52
 
1.2%
300000 52
 
1.2%
3200000 50
 
1.2%
200000 50
 
1.2%
Other values (268) 3708
86.9%
ValueCountFrequency (%)
-100000 28
0.7%
0 45
1.1%
100000 60
1.4%
200000 50
1.2%
300000 52
1.2%
400000 66
1.5%
500000 63
1.5%
600000 56
1.3%
700000 52
1.2%
800000 45
1.1%
ValueCountFrequency (%)
29100000 1
< 0.1%
28700000 1
< 0.1%
28500000 2
< 0.1%
28400000 1
< 0.1%
28300000 1
< 0.1%
28200000 2
< 0.1%
28000000 1
< 0.1%
27600000 2
< 0.1%
27500000 1
< 0.1%
27400000 1
< 0.1%

commercial_assets_value
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct188
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4973155.3
Minimum0
Maximum19400000
Zeros107
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2023-09-05T16:44:25.876301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200000
Q11300000
median3700000
Q37600000
95-th percentile13900000
Maximum19400000
Range19400000
Interquartile range (IQR)6300000

Descriptive statistics

Standard deviation4388966.1
Coefficient of variation (CV)0.88253148
Kurtosis0.1008126
Mean4973155.3
Median Absolute Deviation (MAD)2800000
Skewness0.95779089
Sum2.12304 × 1010
Variance1.9263023 × 1013
MonotonicityNot monotonic
2023-09-05T16:44:26.244241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 107
 
2.5%
200000 101
 
2.4%
100000 100
 
2.3%
300000 90
 
2.1%
500000 83
 
1.9%
800000 76
 
1.8%
700000 74
 
1.7%
400000 71
 
1.7%
600000 70
 
1.6%
1000000 67
 
1.6%
Other values (178) 3430
80.3%
ValueCountFrequency (%)
0 107
2.5%
100000 100
2.3%
200000 101
2.4%
300000 90
2.1%
400000 71
1.7%
500000 83
1.9%
600000 70
1.6%
700000 74
1.7%
800000 76
1.8%
900000 52
1.2%
ValueCountFrequency (%)
19400000 1
 
< 0.1%
19000000 3
0.1%
18900000 1
 
< 0.1%
18800000 2
< 0.1%
18700000 1
 
< 0.1%
18500000 4
0.1%
18400000 3
0.1%
18300000 1
 
< 0.1%
18200000 1
 
< 0.1%
17900000 3
0.1%

luxury_assets_value
Real number (ℝ)

HIGH CORRELATION 

Distinct379
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15126306
Minimum300000
Maximum39200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2023-09-05T16:44:26.600590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile1900000
Q17500000
median14600000
Q321700000
95-th percentile31300000
Maximum39200000
Range38900000
Interquartile range (IQR)14200000

Descriptive statistics

Standard deviation9103753.7
Coefficient of variation (CV)0.6018491
Kurtosis-0.73805613
Mean15126306
Median Absolute Deviation (MAD)7100000
Skewness0.3222075
Sum6.45742 × 1010
Variance8.2878331 × 1013
MonotonicityNot monotonic
2023-09-05T16:44:26.980282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6200000 26
 
0.6%
2900000 26
 
0.6%
20400000 26
 
0.6%
12300000 24
 
0.6%
12000000 24
 
0.6%
14900000 24
 
0.6%
7100000 23
 
0.5%
17200000 23
 
0.5%
14100000 22
 
0.5%
9900000 22
 
0.5%
Other values (369) 4029
94.4%
ValueCountFrequency (%)
300000 4
 
0.1%
400000 10
0.2%
500000 14
0.3%
600000 14
0.3%
700000 14
0.3%
800000 16
0.4%
900000 13
0.3%
1000000 8
0.2%
1100000 19
0.4%
1200000 16
0.4%
ValueCountFrequency (%)
39200000 1
 
< 0.1%
39100000 1
 
< 0.1%
38600000 2
 
< 0.1%
38200000 4
0.1%
38100000 5
0.1%
38000000 1
 
< 0.1%
37900000 2
 
< 0.1%
37800000 2
 
< 0.1%
37700000 1
 
< 0.1%
37600000 2
 
< 0.1%

bank_asset_value
Real number (ℝ)

HIGH CORRELATION 

Distinct146
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4976692.4
Minimum0
Maximum14700000
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2023-09-05T16:44:27.335508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile500000
Q12300000
median4600000
Q37100000
95-th percentile11100000
Maximum14700000
Range14700000
Interquartile range (IQR)4800000

Descriptive statistics

Standard deviation3250185.3
Coefficient of variation (CV)0.65308141
Kurtosis-0.39727743
Mean4976692.4
Median Absolute Deviation (MAD)2400000
Skewness0.56072501
Sum2.12455 × 1010
Variance1.0563705 × 1013
MonotonicityNot monotonic
2023-09-05T16:44:27.693633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400000 63
 
1.5%
4900000 63
 
1.5%
3600000 63
 
1.5%
4500000 61
 
1.4%
1600000 60
 
1.4%
5400000 59
 
1.4%
900000 58
 
1.4%
1300000 57
 
1.3%
1500000 54
 
1.3%
3400000 54
 
1.3%
Other values (136) 3677
86.1%
ValueCountFrequency (%)
0 8
 
0.2%
100000 31
0.7%
200000 54
1.3%
300000 50
1.2%
400000 50
1.2%
500000 40
0.9%
600000 50
1.2%
700000 42
1.0%
800000 39
0.9%
900000 58
1.4%
ValueCountFrequency (%)
14700000 2
< 0.1%
14600000 2
< 0.1%
14400000 1
 
< 0.1%
14300000 1
 
< 0.1%
14200000 2
< 0.1%
14100000 3
0.1%
14000000 3
0.1%
13900000 4
0.1%
13800000 2
< 0.1%
13700000 1
 
< 0.1%

loan_status
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.3 KiB
Approved
2656 
Rejected
1613 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters38421
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Approved
2nd row Rejected
3rd row Rejected
4th row Rejected
5th row Rejected

Common Values

ValueCountFrequency (%)
Approved 2656
62.2%
Rejected 1613
37.8%

Length

2023-09-05T16:44:28.011687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T16:44:28.250175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
approved 2656
62.2%
rejected 1613
37.8%

Most occurring characters

ValueCountFrequency (%)
e 7495
19.5%
p 5312
13.8%
4269
11.1%
d 4269
11.1%
A 2656
 
6.9%
r 2656
 
6.9%
o 2656
 
6.9%
v 2656
 
6.9%
R 1613
 
4.2%
j 1613
 
4.2%
Other values (2) 3226
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29883
77.8%
Space Separator 4269
 
11.1%
Uppercase Letter 4269
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7495
25.1%
p 5312
17.8%
d 4269
14.3%
r 2656
 
8.9%
o 2656
 
8.9%
v 2656
 
8.9%
j 1613
 
5.4%
c 1613
 
5.4%
t 1613
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 2656
62.2%
R 1613
37.8%
Space Separator
ValueCountFrequency (%)
4269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34152
88.9%
Common 4269
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7495
21.9%
p 5312
15.6%
d 4269
12.5%
A 2656
 
7.8%
r 2656
 
7.8%
o 2656
 
7.8%
v 2656
 
7.8%
R 1613
 
4.7%
j 1613
 
4.7%
c 1613
 
4.7%
Common
ValueCountFrequency (%)
4269
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7495
19.5%
p 5312
13.8%
4269
11.1%
d 4269
11.1%
A 2656
 
6.9%
r 2656
 
6.9%
o 2656
 
6.9%
v 2656
 
6.9%
R 1613
 
4.2%
j 1613
 
4.2%
Other values (2) 3226
8.4%

Interactions

2023-09-05T16:44:16.563197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:53.558142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:55.976130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:58.427815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:01.003977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:03.597791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:06.155717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:08.653994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:11.182324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:13.942093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:16.800087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:53.784822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:56.198382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:58.651209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:01.234603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:03.828052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:06.380048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:08.882238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:11.420734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:14.181398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:17.043058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:54.008060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:56.426078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:58.883785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:01.479637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:04.066022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:06.614006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:09.120943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:11.672293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:14.426421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:17.284597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:54.235560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:56.659918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:59.118386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:01.728909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:04.321399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:06.855108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:09.361484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:11.922028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:14.676774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:17.553761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:54.487679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:56.918729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:59.377938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:02.003700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:04.594767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:07.119867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:09.627965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:12.336722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:14.949073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:17.817767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:54.733076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:57.167425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:59.629548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:02.272062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:04.851494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:07.376131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:09.883069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:12.600020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:15.219352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:18.069371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:54.967555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:57.405654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:59.870381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:02.522618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:05.099850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:07.617852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:10.133536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:12.854812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:15.474768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:18.328035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:55.212425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:57.653597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:00.117736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:02.787796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:05.352832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:07.866944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:10.380255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:13.117199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:15.735639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:18.603047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:55.472109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:57.915126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:00.496921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:03.059448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:05.625012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:08.134934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:10.652683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:13.391434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:16.013750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:18.875423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:55.730884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:43:58.179344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:00.754617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:03.332999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:05.894477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:08.399345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:10.918060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:13.672762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T16:44:16.292430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-05T16:44:28.463700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
loan_idno_of_dependentsincome_annumloan_amountloan_termcibil_scoreresidential_assets_valuecommercial_assets_valueluxury_assets_valuebank_asset_valueeducationself_employedloan_status
loan_id1.0000.0060.0130.0120.0100.0160.0260.0210.0010.0150.0000.0120.038
no_of_dependents0.0061.0000.007-0.001-0.020-0.0100.0120.0040.0050.0130.0000.0240.000
income_annum0.0130.0071.0000.9410.011-0.0230.6380.6430.9430.8790.0000.0350.020
loan_amount0.012-0.0010.9411.0000.012-0.0200.6130.6220.8940.8400.0280.0000.052
loan_term0.010-0.0200.0110.0121.0000.0080.003-0.0010.0080.0140.0000.0390.184
cibil_score0.016-0.010-0.023-0.0200.0081.000-0.031-0.001-0.030-0.0200.0000.0000.875
residential_assets_value0.0260.0120.6380.6130.003-0.0311.0000.4480.6130.5690.0000.0000.016
commercial_assets_value0.0210.0040.6430.622-0.001-0.0010.4481.0000.6190.5890.0000.0280.018
luxury_assets_value0.0010.0050.9430.8940.008-0.0300.6130.6191.0000.8360.0140.0000.040
bank_asset_value0.0150.0130.8790.8400.014-0.0200.5690.5890.8361.0000.0000.0000.000
education0.0000.0000.0000.0280.0000.0000.0000.0000.0140.0001.0000.0170.000
self_employed0.0120.0240.0350.0000.0390.0000.0000.0280.0000.0000.0171.0000.000
loan_status0.0380.0000.0200.0520.1840.8750.0160.0180.0400.0000.0000.0001.000

Missing values

2023-09-05T16:44:19.270360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-05T16:44:19.767722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

loan_idno_of_dependentseducationself_employedincome_annumloan_amountloan_termcibil_scoreresidential_assets_valuecommercial_assets_valueluxury_assets_valuebank_asset_valueloan_status
012GraduateNo96000002990000012778240000017600000227000008000000Approved
120Not GraduateYes41000001220000084172700000220000088000003300000Rejected
233GraduateNo91000002970000020506710000045000003330000012800000Rejected
343GraduateNo8200000307000008467182000003300000233000007900000Rejected
455Not GraduateYes98000002420000020382124000008200000294000005000000Rejected
560GraduateYes4800000135000001031968000008300000137000005100000Rejected
675GraduateNo87000003300000046782250000014800000292000004300000Approved
782GraduateYes57000001500000020382132000005700000118000006000000Rejected
890GraduateYes80000022000002078213000008000002800000600000Approved
9105Not GraduateNo11000004300000103883200000140000033000001600000Rejected
loan_idno_of_dependentseducationself_employedincome_annumloan_amountloan_termcibil_scoreresidential_assets_valuecommercial_assets_valueluxury_assets_valuebank_asset_valueloan_status
425942600Not GraduateYes45000001150000014509134000002300000154000005900000Rejected
426042615GraduateNo880000029300000105601680000013900000311000009900000Approved
426142623GraduateYes3000000750000068811400000450000061000002300000Approved
426242635GraduateNo13000003000000205401000000230000032000001900000Rejected
426342643GraduateNo5000000127000001486547000008100000195000006300000Approved
426442655GraduateYes100000023000001231728000005000003300000800000Rejected
426542660Not GraduateYes3300000113000002055942000002900000110000001900000Approved
426642672Not GraduateNo65000002390000018457120000012400000181000007300000Rejected
426742681Not GraduateNo41000001280000087808200000700000141000005800000Approved
426842691GraduateNo9200000297000001060717800000118000003570000012000000Approved